CN104316025A - System for estimating height of sea wave based on attitude information of ship - Google Patents

Abstract

The invention belongs to the field of estimation on attitude information of a ship, and in particular relates to a system for estimating height of sea wave based on attitude information of the ship. The system comprises an unscented kalman filter, a sea wave disturbance estimator, a sea wave height estimator, and an acceleration sensor of a measuring system on the ship, wherein an inertia gyroscope measures the attitude information of the ship, the sea wave disturbance estimator receives the controlled quantity acting on the ship and simultaneously receives an estimated value of the hydrodynamic coefficient output by the unscented kalman filter, so as to obtain the sea wave disturbance force and force moment estimation sequences output by the sea wave disturbance estimator, the sea wave height estimator receives the sea wave disturbance force and force moment estimation sequences to obtain the sea wave height estimation sequences. The system can well solve the problems that the parameters of kinematic equation of the ship are unknown and uncertain and indirectly solve the sea wave height by utilizing the sea wave disturbance acting on the ship and derived by the attitude of the ship in an inverted mode, so as to achieve high computational accuracy.

Description

A kind of system high based on Attitude information estimation wave wave

Technical field

The invention belongs to Attitude information and estimate field, specifically a kind of system high based on Attitude information estimation wave wave.

Background technology

Ocean is the important ingredient in the world, to study it, first wave will be considered, we know, wave is the general spontaneous phenomenon of one in ocean, it is produced by the irregular fluctuation of seawater, and affect the afloat various activity of people, no matter be civilian fishing boat, or ships used for military purposes, the Shi Douhui that rides the sea produces because of the impact of wave and acutely sways, light then affect navigation, heavy then ship may be damaged or make it sink, therefore in order to reduce the menace of wave to sail ship, considerable meaning is had to the research of wave characteristic.

In understanding wave characteristic, traditional system uses some instruments to measure the related data of wave, many ships are had to use wave height recorder to measure wave in practice high, for this situation, equally also there will be some problems, as the restriction in the fault of wave height recorder itself and unrestrained level, that is wave height recorder is not be all under any circumstance operable, it has certain limitation, certainly more advanced ship is also had to be equipped with ship wave photographic measurement system, although this system also can achieve the goal, but due to its complicacy and expensive, make it can be able to not use at all ships, this also just limits this systematic difference scope.Consider these systems all very not satisfactory, therefore find out and better measure the high system of wave wave, increasing the security of boats and ships when navigating by water, being still current important problem.

For in the system that traditional use apparatus measures wave wave is high, the principle of work of wave height recorder needs in use to throw in measure marine site, wherein sensor is utilized to carry out survey calculation and export corresponding unrestrained high data, reclaim after to be measured, and same principle, during using the hull of marine navigation as a wave height recorder, utilize some of hull self the navigation wave of attitude variablees to wave is high and calculate, equally also can obtain corresponding information, and decrease the process of throwing in reclaiming, convenient.

The ship motion equation of the present invention by setting up, application Unscented kalman filtering device carries out identification to hull model parameter, and instead releases the wave disturbance suffered by hull according to this.Find out the relation between wave disturbance and wave wave height by the system of utilization neural network, indirectly obtain wave wave high.This does not wherein use new surveying instrument, just uses some navigational parameters that hull provides in the process of marine navigation, and this method is a kind of safer, wave wave height measuring system easily.

Summary of the invention

What the object of the present invention is to provide a kind of higher computational accuracy estimates based on Attitude information the system that wave wave is high.

The object of the present invention is achieved like this

The system that wave wave is high is estimated based on Attitude information, comprise Unscented kalman filtering device (1), estimation of sea interference device (2), the wave high estimator of wave (3), the acceleration transducer of measuring system on ship, inertial gyroscope measures Attitude information: surging speed, swaying speed, yaw angle speed, pass to Unscented kalman filtering device (1), effect controlled quentity controlled variable is aboard ship sent to Unscented kalman filtering device (1) and estimation of sea interference device (2) respectively, Unscented kalman filtering device (1) obtains hydrodynamic force coefficient estimated value after receiving hull attitude information and effect controlled quentity controlled variable aboard ship simultaneously, the hydrodynamic force coefficient estimated value of the output of Unscented kalman filtering device (1) is received while estimation of sea interference device (2) reception effect controlled quentity controlled variable aboard ship, obtain wave disturbance power, moment estimated sequence that estimation of sea interference device (2) exports, after the high estimator of wave (3) receives wave disturbance power, moment estimated sequence, obtain the high estimated sequence of wave wave.

Unscented kalman filtering device obtains sigma point by sigma point generator, acquisition state a step of forecasting and state a step of forecasting covariance is upgraded through the nonlinear state function time of carrying out, carry out measurement updaue through non-linear measurement function and obtain the prediction of output, prediction of output autocovariance and the mutual variance of the prediction of output, after giving state estimator by real output value, the prediction of output, prediction of output autocovariance, prediction of output cross covariance, state a step of forecasting and state a step of forecasting covariance, obtain hydrodynamic force coefficient estimated value; Described nonlinear state function and non-linear measurement function are:

X _kthat n ties up state vector, y _kthat r ties up output vector, θ _kthat p ties up unknown parameter vector, w _kthat n ties up state-noise vector, v _kthat r ties up measurement noise vector, the state vector of n+p dimension, η _kthat p ties up state-noise vector, herein, hypothetical sequence w _k, η _kwith v _kbe discrete white Gaussian noise;

Sigma point generator adopts symmetric sampling strategy to obtain sigma point:

Time upgrades and refers to that each point concentrated by Sigma point is brought in nonlinear state function,

State a step of forecasting is

State a step of forecasting covariance is

Measurement updaue refers to the state a step of forecasting in upgrading based on the time with state a step of forecasting covariance P ' _{x, k}, utilize sigma point generator to obtain sigma point propagate through non-linear measurement function

${\mathrm{\γ}}_{k|k-1}^i=h\left(x_{k|k-1}^i\right)$

The prediction of output is ${\hat{y}}_k^{\′}=\underset{i=1}{\overset{n+p}{\mathrm{\Σ}}}{W}_i^{\left(m\right)}{\mathrm{\γ}}_{k|k-1}^i,$

Prediction of output autocovariance is $P_{y,k}=\underset{i=0}{\overset{n+p}{\mathrm{\Σ}}}{W}_i^{\left(c\right)}[{\mathrm{\γ}}_{k|k-1}^i-{\hat{y}}_k^{\′}]{[{\mathrm{\γ}}_{k|k-1}^i-{\hat{y}}_k^{\′}]}^T,$

Prediction of output cross covariance is

State estimator refers to and uses the actual value exported to carry out the state posterior estimate of corrected Calculation:

Described real output value refers to the Attitude information obtained by measuring system, pole surging speed, swaying speed and yaw angle speed, measuring system refers to linear acceleration transducer, inertial gyroscope, state posterior estimate refers to hydrodynamic force coefficient and Attitude identifier, and estimation of sea interference device is:

${\widehat{\mathrm{\τ}}}_E\left(k\right)=\hat{M}\hat{x}(k+1)+\hat{D}\hat{x}\left(k\right)\text{+}\hat{C}\left(k\right)\hat{x}\left(k\right)-\mathrm{\τ}\left(k\right),$

The high estimator of wave wave is:

$y_k\left(x_j\right)=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{w}_{\mathrm{ik}}\exp (-\frac{1}{2{\mathrm{\σ}}^2}{\left|\right|x_j-c_i\left|\right|}^2)k=\mathrm{1,2},...,p,j=\mathrm{1,2},...b.$

For asking for, wave wave is high, and the present invention has following advantage:

This system produces for solving the sailing hull measurement wave high relative complex of wave, it carries out survey calculation as wave height recorder certainly with hull, reduce complicacy when traditional instrument is measured, in addition, owing to setting up the non-linear of ship motion model, utilize Unscented kalman filtering device to complete the identification of parameter, ship motion equation parameter used the unknown and probabilistic situation can well be solved, finally utilize the wave disturbance suffered by the anti-hull released of Attitude indirectly to obtain wave wave high, reach higher computational accuracy.

Accompanying drawing explanation

Fig. 1 estimates based on Attitude information the structured flowchart that wave wave is high;

Fig. 2 is that ship motion equation parameter estimates structural representation;

Fig. 3 is UKF filter identification hydrodynamic force coefficient relative error;

Fig. 4 is the high comparison diagram with actual value of the wave wave that finally estimated by neural network.

Concrete implementation system

Below in conjunction with accompanying drawing, the present invention is described further.

For traditional deficiency of ocean wave measurement system and the limitation of use, the present invention proposes and use Attitude indirectly to obtain the high system of wave wave by calculating.The object of the present invention is achieved like this:

Comprise Unscented kalman filtering device (1), estimation of sea interference device (2), the wave high estimator of wave (3).On ship, measuring system (acceleration transducer, inertial gyroscope) measures Attitude information (surging speed, swaying speed, yaw angle speed), pass to Unscented kalman filtering device (1), effect controlled quentity controlled variable aboard ship sends to Unscented kalman filtering device (1) and estimation of sea interference device (2) respectively, and Unscented kalman filtering device (1) obtains hydrodynamic force coefficient estimated value after receiving hull attitude information and effect controlled quentity controlled variable aboard ship simultaneously; The output data (hydrodynamic force coefficient estimated value) of Unscented kalman filtering device (1) are also received while estimation of sea interference device (2) reception effect controlled quentity controlled variable aboard ship, obtain the output data (wave disturbance power, moment estimated sequence) of estimation of sea interference device (2), most back rise is over-evaluated after gauge (3) receives wave disturbance (power, moment) estimated sequence, obtains the high estimated sequence of wave wave.

Of the present invention based on Attitude information estimate wave wave high system can also comprise:

1, described Unscented kalman filtering device obtains sigma point by sigma point generator, acquisition state a step of forecasting and state a step of forecasting covariance is upgraded through the nonlinear state function time of carrying out, carry out measurement updaue through non-linear measurement function and obtain the prediction of output, prediction of output autocovariance and the mutual variance of the prediction of output, after giving state estimator by real output value, the prediction of output, prediction of output autocovariance, prediction of output cross covariance, state a step of forecasting and state a step of forecasting covariance, obtain state posterior estimate.

2, described nonlinear state function and non-linear measurement function are:

3, described sigma point generator adopts symmetric sampling strategy to obtain sigma point:

4, the described time upgrades and refers to that each point concentrated by Sigma point is brought in nonlinear state function,

State a step of forecasting is

State a step of forecasting covariance is

During 5, described measurement updaue refers to and upgrades based on claim 5 time with P ' _x,ksigma point generator is utilized to obtain sigma point propagate through non-linear measurement function

${\mathrm{\γ}}_{k|k-1}^i=h\left(x_{k|k-1}^i\right)---\left(15\right)$

The prediction of output is ${\hat{y}}_k^{\′}=\underset{i=1}{\overset{n+p}{\mathrm{\Σ}}}{W}_i^{\left(m\right)}{\mathrm{\γ}}_{k|k-1}^i---\left(16\right)$

Prediction of output autocovariance is $P_{y,k}=\underset{i=0}{\overset{n+p}{\mathrm{\Σ}}}{W}_i^{\left(c\right)}[{\mathrm{\γ}}_{k|k-1}^i-{\hat{y}}_k^{\′}]{[{\mathrm{\γ}}_{k|k-1}^i-{\hat{y}}_k^{\′}]}^T---\left(17\right)$

Prediction of output cross covariance is

6, described state estimator refers to and uses the actual value exported to carry out the state posterior estimate of corrected Calculation:

7, described real output value refers to the Attitude information obtained by measuring system

8, described Attitude information refers to surging speed, swaying speed and yaw angle speed.

9, described measuring system refers to linear acceleration transducer, inertial gyroscope.

10, described state posterior estimate refers to hydrodynamic force coefficient and Attitude identifier.

11, described estimation of sea interference device is:

${\widehat{\mathrm{\τ}}}_E\left(k\right)=\hat{M}\hat{x}(k+1)+\hat{D}\hat{x}\left(k\right)\text{+}\hat{C}\left(k\right)\hat{x}\left(k\right)-\mathrm{\τ}\left(k\right)---\left(20\right)$

12, the described high estimator of wave wave is:

$y_k\left(x_j\right)=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{w}_{\mathrm{ik}}\exp (-\frac{1}{2{\mathrm{\σ}}^2}{\left|\right|x_j-c_i\left|\right|}^2)k=\mathrm{1,2},...,p,j=\mathrm{1,2},...b---\left(21\right)$

Principle of work of the present invention is: carry out parameter augmentation to former nonlinear state equation i.e. ship motion equation, obtain corresponding state equation and observation equation, and discretize; Expansion Unscented kalman (UKF) nonlinear observer is used to carry out identification to the parameter of ship motion equation and to Attitude information filter; Attitude information is utilized to obtain the wave disturbance suffered by corresponding hull by estimation of sea interference device; Use the wave high data of wave and the wave disturbance suffered by corresponding hull to train neural network (the high estimator of wave wave), using wave disturbance as input, obtain corresponding wave wave high.

What the present invention described is the high estimating system of a kind of wave wave, compared with traditional wave wave height measuring system, and the situation that wave wave current when this system can obtain hull navigation fast and is accurately high, easy to operate and save cost.As shown in Figure 1, concrete step is as follows for design proposal of the present invention:

1. the augmentation of ship motion equation parameter and the preparation of identification, detailed process is as follows:

When using Unscented kalman (UKF) wave filter to process the parameter identification problem of nonlinear equation, main through following two stages: first is the forecast stage, in this stage, the mainly predicted value of computing mode and the covariance of now corresponding state forecast error; In second stage, what mainly complete is the step of updating of state forecast error and covariance, according to the filter gain by the UKF wave filter calculated, upgrades it, also will complete the renewal to forecast state simultaneously.

Suppose that θ is the parameter vector that will estimate, augmentation is carried out to former nonlinear state equation, namely parameter vector θ is carried out together with previous status the expansion of nonlinear state equation as state, then UKF filtering is carried out to the nonlinear equation under this extended mode, now also this filtering can be called expansion UKF filtering.By estimating that the minimum variance of θ has carried out the optimal estimation to unknown parameter vector.

Assuming that being described below of discrete system, and wherein include unknown parameter:

$\left\{\begin{array}{c}{x}_{k+1}=f(x_k,{\mathrm{\θ}}_k)+w_k\\ y_k=h(x_k,{\mathrm{\θ}}_k)+v_k\end{array}\right.---\left(1\right)$

In formula, x _kthat n ties up state vector, y _kthat r ties up output vector, θ _kthat p ties up unknown parameter vector, w _kthat n ties up state-noise vector, v _kthat r ties up measurement noise vector.

Estimate with utilization UKF for convenience of description, the separate manufacturing firms described by formula (1) is expressed as:

In above formula, the state vector of n+p dimension, η _kthat p ties up state-noise vector, herein, hypothetical sequence w _k, η _kwith v _kbe discrete white Gaussian noise.

2. the identification of pair ship motion equation hydrodynamic force coefficient, detailed process is as follows:

For document " Gianluca Antonelli; Stefano Chiaverini; Nilanjan Sarkar, Michael West.Adaptive Control of an Autonomous Underwater Vehicle:Experimental Results on ODIN [J].

TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2001,9 (5) " the spherical underwater robot ODIN provided, suppose that ODIN remains on a certain depthkeeping and navigates by water, the equation of motion of its surface level is:

$\stackrel{\·}{x}\left(x\right)x+D\left(x\right)x={\mathrm{\τ}}_E+\mathrm{\τ}---\left(3\right)$

In formula: x=[u v r] ^t, u, v, r are ODIN surging speed, swaying speed, yaw angle speed; M is inertial matrix, comprises additional mass; C (x) is centripetal force and coriolis force matrix, comprises centripetal force and the coriolis force of additional mass generation; D (x) is hydrodynamic drag and lift matrix; τ _efor wave disturbance; τ represents the control vector acted under ODIN carrier coordinate system, i.e. force and moment.

$M=\left[\begin{array}{ccc}m-X_{\stackrel{\·}{u}}& 0& 0\\ 0& m-Y_{\stackrel{\·}{v}}& m{x}_G-Y_{\stackrel{\·}{r}}\\ 0& m{x}_G-N_{\stackrel{\·}{v}}& I_z-N_{\stackrel{\·}{r}}\end{array}\right]---\left(4\right)$

$C\left(x\right)=\left[\begin{array}{ccc}0& 0& -m(x_{G}r+v)\\ 0& 0& \mathrm{mu}\\ m(x_{G}r+v)& -\mathrm{mu}& 0\end{array}\right]---\left(5\right)$

The state of the ODIN considered and u, v, r are all smaller, and therefore Second-order Damped matrix can be ignored, then:

$D\left(x\right)=-\left[\begin{array}{ccc}{X}_u& 0& 0\\ 0& Y_v& Y_r\\ 0& N_v& N_r\end{array}\right]---\left(6\right)$

In formula, what m represented is hull mass, x _gwhat represent is centre of buoyancy x-axis coordinate in body coordinate system, I _zwhat represent is the moment of inertia of hull about z-axis.

Expansion UKF wave filter is used to carry out identification to the parameter of ship motion equation, and set up identification model, as shown in Figure 2, when using the parameter of expansion Unscented kalman filtering device to the equation of motion to estimate, the parameter that estimate mainly contains its identification structure schematic diagram unknown parameter to be estimated is designated as vectorial θ, order $\mathrm{\θ}=\begin{array}{c}{\left[\begin{array}{cccccccccc}{X}_{\stackrel{\·}{u}}& X_u& Y_{\stackrel{\·}{v}}& Y_{\stackrel{\·}{r}}& Y_r& Y_v& N_{\stackrel{\·}{v}}& N_{\stackrel{\·}{r}}& N_v& N_r\end{array}\right]}^T\end{array}.$

Formula (3) can be written as:

$\stackrel{\·}{x=}{M}^{-1}[{\mathrm{\τ}}_E+\mathrm{\τ}-D\left(x\right)x-C\left(x\right)x]---\left(7\right)$

Thus obtain the differential equation that is:

f(x,θ)＝M ^-1[τ _E+τ-D(x)x-C(x)x] (8)

Equation of motion hydrodynamic force coefficient θ is expanded in state variable x, and carries out discretize and can obtain nonlinear discrete state equation and corresponding measurement equation, see formula (2), in formula y _kbe this discrete system measurement vector and for y _k=[u _kv _kr _k] ^t.Then UKF filtering theory is applied in formula (2), just can obtains hydrodynamic force coefficient by identification with , will with bring formula (4) ~ formula (6) into obtain identification result relative error is in table 1.

UKF filtering is as follows:

(1) sigma point generator adopts symmetric sampling strategy to obtain sigma point:

In formula for k-1 moment state average and covariance, n+p=13, ρ are scale-up factor, for regulating with between distance, for on Square-Rooting Matrices i-th row.

(2) time upgrades:

Each point concentrated by Sigma point is brought in nonlinear state function and goes,

State a step of forecasting is

In formula

$W_0^{\left(m\right)}=\mathrm{\ρ}/(n+\mathrm{\ρ})$

$W_i^{\left(m\right)}=1/\left[2(n+\mathrm{\ρ})\right],i=1,...,n+p$

State a step of forecasting covariance is

In formula $W_0^{\left(c\right)}=\mathrm{\ρ}/(n+\mathrm{\ρ})+(1+{\mathrm{\α}}^2+\mathrm{\β}),W_i^{\left(c\right)}=W_i^{\left(m\right)},$ Q _kfor variance, β, α determine the distribution situation of sigma point around.

(3) measurement updaue:

Upgrade in (2) based on the time with P ' _{x, k}, utilize sigma point generator to obtain sigma point propagate through non-linear measurement function and be

${\mathrm{\γ}}_{k|k-1}^i=h\left(x_{k|k-1}^i\right)---\left(15\right)$

The prediction of output is ${\hat{y}}_k^{\′}=\underset{i=1}{\overset{n+p}{\mathrm{\Σ}}}{W}_i^{\left(m\right)}{\mathrm{\γ}}_{k|k-1}^i---\left(16\right)$

Prediction of output autocovariance is $P_{y,k}=\underset{i=0}{\overset{n+p}{\mathrm{\Σ}}}{W}_i^{\left(c\right)}[{\mathrm{\γ}}_{k|k-1}^i-{\hat{y}}_k^{\′}]{[{\mathrm{\γ}}_{k|k-1}^i-{\hat{y}}_k^{\′}]}^T---\left(17\right)$

Prediction of output cross covariance is

R in formula _kfor v _kvariance.

(4) state estimator

The actual value exported is used to carry out the state posterior estimate of corrected Calculation:

In formula state covariance is updated to P _k=P ' _x,k-KP _y,kk ^t.

3. utilize estimation of sea interference device to carry out the reckoning of wave disturbance suffered by hull, detailed process is as follows:

Hydrodynamic force coefficient according to using UKF wave filter to estimate obtains concrete ship motion equation, and the wave disturbance suffered by hull embodies to some extent in this equation, now can show that the discretization equation of estimation of sea interference suffered by hull is by formula (7):

${\widehat{\mathrm{\τ}}}_E\left(k\right)=\hat{M}\hat{x}(k+1)+\hat{D}\hat{x}\left(k\right)\text{+}\hat{C}\left(k\right)\hat{x}\left(k\right)-\mathrm{\τ}\left(k\right)---\left(20\right)$

In formula $\hat{M}=\left[\begin{array}{ccc}m-{\hat{X}}_{\stackrel{\·}{u}}& 0& 0\\ 0& m-{\hat{Y}}_{\stackrel{\·}{v}}& {\mathrm{mx}}_G-{\hat{Y}}_{\stackrel{\·}{r}}\\ 0& {\mathrm{mx}}_G-{\hat{N}}_{\stackrel{\·}{v}}& I_z-{\hat{N}}_{\stackrel{\·}{r}}\end{array}\right],\hat{C}\left(x\right)=\left[\begin{array}{ccc}0& 0& -m(x_G\hat{r}+\hat{v})\\ 0& 0& m\hat{u}\\ m(x_G\hat{r}+\hat{v})& -m\hat{v}& 0\end{array}\right],$ $\hat{D}\left(x\right)=-\left[\begin{array}{ccc}{\hat{X}}_u& 0& 0\\ 0& {\hat{Y}}_v& {\hat{Y}}_r\\ 0& {\hat{N}}_v& {\hat{N}}_r\end{array}\right],\hat{x}={\left[\hat{u}\hat{v}\hat{r}\right]}^T.$

When navigating by water, run attitude information according to hull, then wave disturbance suffered under can estimating present case, and then the high at the wave wave in each moment of wave can be drawn according to these disturbances.

4. the high estimator of wave wave estimates that wave wave is high according to wave disturbance and the unrestrained high relation of wave, and detailed process is as follows:

Hull is when marine advancing, the effect of wave disturbance can be subject to, from experiment, different ships is under identical sea wave disturbance, and the wave disturbance suffered by hull also can be different, therefore, when the estimation of sea interference wave wave suffered by hull is high, ready-made effective computing formula or experience can not sought, and in order to effectively solve this difficult problem, have been incorporated herein radial basis function (RBF) neural network.Utilize the learning ability of neural network to solve the problem of relation the unknown between wave disturbance and wave wave height, obtain the wave of wave according to this relation further high.

RBF neural is as follows:

$y_k\left(x_j\right)=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{w}_{\mathrm{ik}}\exp (-\frac{1}{2{\mathrm{\σ}}^2}{\left|\right|x_j-c_i\left|\right|}^2)k=\mathrm{1,2},...,p,j=\mathrm{1,2},...b---\left(21\right)$

X in formula _jbe a jth input amendment, σ is Gaussian function variance, and m refers to node in hidden layer, norm || x _j-c _i|| expression be input quantity x _jto Gaussian bases center c _idistance.W _iknet connection weights, y _kthe actual output of a kth output node of the network corresponding with input amendment.

By wave disturbance and the high data of corresponding wave wave as the input of RBF, output quantity, neural network is trained.But because input data are not identical with the dimension exporting data, therefore, need to be normalized these raw data.Then the data asked for are carried out renormalization and export that can to obtain wave wave high, the high estimated sequence of wave wave obtained when what Fig. 4 represented is 5 grades of seas are clear.

Claims (5)

1. estimate based on Attitude information the system that wave wave is high for one kind, it is characterized in that: comprise Unscented kalman filtering device (1), estimation of sea interference device (2), the wave high estimator of wave (3), the acceleration transducer of measuring system on ship, inertial gyroscope measures Attitude information: surging speed, swaying speed, yaw angle speed, pass to Unscented kalman filtering device (1), effect controlled quentity controlled variable is aboard ship sent to Unscented kalman filtering device (1) and estimation of sea interference device (2) respectively, Unscented kalman filtering device (1) obtains hydrodynamic force coefficient estimated value after receiving hull attitude information and effect controlled quentity controlled variable aboard ship simultaneously, the hydrodynamic force coefficient estimated value of the output of Unscented kalman filtering device (1) is received while estimation of sea interference device (2) reception effect controlled quentity controlled variable aboard ship, obtain wave disturbance power, moment estimated sequence that estimation of sea interference device (2) exports, after the high estimator of wave (3) receives wave disturbance power, moment estimated sequence, obtain the high estimated sequence of wave wave.

2. a kind of system high based on Attitude information estimation wave wave according to claim 1, it is characterized in that: described Unscented kalman filtering device obtains sigma point by sigma point generator, acquisition state a step of forecasting and state a step of forecasting covariance is upgraded through the nonlinear state function time of carrying out, carry out measurement updaue through non-linear measurement function and obtain the prediction of output, prediction of output autocovariance and the mutual variance of the prediction of output, by real output value, the prediction of output, prediction of output autocovariance, prediction of output cross covariance, hydrodynamic force coefficient estimated value is obtained after state a step of forecasting and state a step of forecasting covariance give state estimator, described nonlinear state function and non-linear measurement function are:

X _kthat n ties up state vector, y _kthat r ties up output vector, θ _kthat p ties up unknown parameter vector, w _kthat n ties up state-noise vector, v _kthat r ties up measurement noise vector, the state vector of n+p dimension, η _kthat p ties up state-noise vector, herein, hypothetical sequence w _k, η _kwith v _kbe discrete white Gaussian noise;

Described sigma point generator adopts symmetric sampling strategy to obtain sigma point:

。

3. a kind of system high based on Attitude information estimation wave wave according to claim 2, is characterized in that: the described time upgrades and refers to that each point concentrated by Sigma point is brought in nonlinear state function,

State a step of forecasting is

State a step of forecasting covariance is

4. a kind of system high based on Attitude information estimation wave wave according to claim 3, is characterized in that: described measurement updaue refers to the state a step of forecasting in upgrading based on the time with state a step of forecasting covariance P ' _{x, k}, utilize sigma point generator to obtain sigma point propagate through non-linear measurement function

The prediction of output is

Prediction of output autocovariance is

Prediction of output cross covariance is

5. a kind of system high based on Attitude information estimation wave wave according to claim 2, is characterized in that: described state estimator refers to and uses the actual value exported to carry out the state posterior estimate of corrected Calculation:

Described real output value refers to the Attitude information obtained by measuring system, pole surging speed, swaying speed and yaw angle speed, measuring system refers to linear acceleration transducer, inertial gyroscope, state posterior estimate refers to hydrodynamic force coefficient and Attitude identifier, and estimation of sea interference device is:

The high estimator of wave wave is: